Project Homepage: https://github.com/Yinqi93/LUCIDus
Details about LUCID:
The Latent Unknown Clustering Integrating multi-omics Data (LUCID) is a model-based clustering method which jointly estimates the latent clustering structure and the association between the cluster and the outcome of interest. It provides insight of the correlations among genomic data, gene expression and disease phenotype. Currently, LUCID is implemented through the R package LUCIDus hosted by CRAN. The developing version is also available on Github(see homepage). The latest version of LUCID (2.1.0) features a complete statistical analysis toolkit including:
Clustering estimation
Model visualization by a Sankey diagram
Variable selection procedure for both genomics data and gene expression data
Ability to incorporating the missing data in gene expression
Model inference based on bootstrap
Plotly allows to build charts thanks to it plot_ly() function. It offers several type option. surface allows to make surface plots and expect a matrix as input.
Rows and columns actually describe a grid, and the cell value will be mapped to the surface height. Once the chart is done, you can hover and zoom on the chart for more details.
The dygraphs package is an R interface to the dygraphs JavaScript charting library. It provides rich facilities for charting time-series data in R, including: